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Blogs

1. How to Find the Best Theoretical Distribution for Your Data

2. Outlier Detection Using Distribution Fitting in Univariate Datasets

3. Step-by-Step Guide to Generate Synthetic Data by Sampling From Univariate Distributions

Documentation pages

distfit is a python package for probability density fitting of univariate distributions for random variables. With the random variable as an input, distfit can find the best fit for parametric, non-parametric, and discrete distributions.

  • For the parametric approach, the distfit library can determine the best fit across 89 theoretical distributions. To score the fit, one of the scoring statistics for the good-of-fitness test can be used used, such as RSS/SSE, Wasserstein, Kolmogorov-Smirnov (KS), or Energy. After finding the best-fitted theoretical distribution, the loc, scale, and arg parameters are returned, such as mean and standard deviation for normal distribution.

  • For the non-parametric approach, the distfit library contains two methods, the quantile and percentile method. Both methods assume that the data does not follow a specific probability distribution. In the case of the quantile method, the quantiles of the data are modeled whereas for the percentile method, the percentiles are modeled.

  • In case the dataset contains discrete values, the distift library contains the option for discrete fitting. The best fit is then derived using the binomial distribution.

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Installation

Install distfit from PyPI
pip install distfit
Install from github source (beta version)
 install git+https://github.com/erdogant/distfit
Check version
import distfit
print(distfit.__version__)
The following functions are available after installation:
# Import library
from distfit import distfit

dfit = distfit()        # Initialize 
dfit.fit_transform(X)   # Fit distributions on empirical data X
dfit.predict(y)         # Predict the probability of the resonse variables
dfit.plot()             # Plot the best fitted distribution (y is included if prediction is made)

Examples

Example: Quick start to find best fit for your input data
# [distfit] >INFO> fit
# [distfit] >INFO> transform
# [distfit] >INFO> [norm      ] [0.00 sec] [RSS: 0.00108326] [loc=-0.048 scale=1.997]
# [distfit] >INFO> [expon     ] [0.00 sec] [RSS: 0.404237] [loc=-6.897 scale=6.849]
# [distfit] >INFO> [pareto    ] [0.00 sec] [RSS: 0.404237] [loc=-536870918.897 scale=536870912.000]
# [distfit] >INFO> [dweibull  ] [0.06 sec] [RSS: 0.0115552] [loc=-0.031 scale=1.722]
# [distfit] >INFO> [t         ] [0.59 sec] [RSS: 0.00108349] [loc=-0.048 scale=1.997]
# [distfit] >INFO> [genextreme] [0.17 sec] [RSS: 0.00300806] [loc=-0.806 scale=1.979]
# [distfit] >INFO> [gamma     ] [0.05 sec] [RSS: 0.00108459] [loc=-1862.903 scale=0.002]
# [distfit] >INFO> [lognorm   ] [0.32 sec] [RSS: 0.00121597] [loc=-110.597 scale=110.530]
# [distfit] >INFO> [beta      ] [0.10 sec] [RSS: 0.00105629] [loc=-16.364 scale=32.869]
# [distfit] >INFO> [uniform   ] [0.00 sec] [RSS: 0.287339] [loc=-6.897 scale=14.437]
# [distfit] >INFO> [loggamma  ] [0.12 sec] [RSS: 0.00109042] [loc=-370.746 scale=55.722]
# [distfit] >INFO> Compute confidence intervals [parametric]
# [distfit] >INFO> Compute significance for 9 samples.
# [distfit] >INFO> Multiple test correction method applied: [fdr_bh].
# [distfit] >INFO> Create PDF plot for the parametric method.
# [distfit] >INFO> Mark 5 significant regions
# [distfit] >INFO> Estimated distribution: beta [loc:-16.364265, scale:32.868811]

Example: Plot summary of the tested distributions

After we have a fitted model, we can make some predictions using the theoretical distributions. After making some predictions, we can plot again but now the predictions are automatically included.

Example: Make predictions using the fitted distribution

Example: Test for one specific distributions

The full list of distributions is listed here: https://erdogant.github.io/distfit/pages/html/Parametric.html

Example: Test for multiple distributions

The full list of distributions is listed here: https://erdogant.github.io/distfit/pages/html/Parametric.html

Example: Fit discrete distribution
from scipy.stats import binom
# Generate random numbers

# Set parameters for the test-case
n = 8
p = 0.5

# Generate 10000 samples of the distribution of (n, p)
X = binom(n, p).rvs(10000)
print(X)

# [5 1 4 5 5 6 2 4 6 5 4 4 4 7 3 4 4 2 3 3 4 4 5 1 3 2 7 4 5 2 3 4 3 3 2 3 5
#  4 6 7 6 2 4 3 3 5 3 5 3 4 4 4 7 5 4 5 3 4 3 3 4 3 3 6 3 3 5 4 4 2 3 2 5 7
#  5 4 8 3 4 3 5 4 3 5 5 2 5 6 7 4 5 5 5 4 4 3 4 5 6 2...]

# Import distfit
from distfit import distfit

# Initialize for discrete distribution fitting
dfit = distfit(method='discrete')

# Run distfit to and determine whether we can find the parameters from the data.
dfit.fit_transform(X)

# [distfit] >fit..
# [distfit] >transform..
# [distfit] >Fit using binomial distribution..
# [distfit] >[binomial] [SSE: 7.79] [n: 8] [p: 0.499959] [chi^2: 1.11]
# [distfit] >Compute confidence interval [discrete]

Example: Make predictions on unseen data for discrete distribution

Example: Generate samples based on the fitted distribution

Contributors

Setting up and maintaining distfit has been possible thanks to users and contributors. Thanks:

Citation

Please cite distfit in your publications if this is useful for your research. See column right for citation information.

Maintainer

  • Erdogan Taskesen, github: erdogant
  • Contributions are welcome.
  • If you wish to buy me a Coffee for this work, it is very appreciated :)